Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey

TL;DR

This survey reviews the use of agentic graph neural networks in wireless communications, emphasizing their role in enabling edge general intelligence and addressing the challenges of complex, dynamic networks.

Contribution

It introduces the concept of agentic GNNs for scenario- and task-aware wireless network management, integrating AI to enhance adaptability and scalability.

Findings

GNNs effectively model complex network topologies.

Agentic GNNs enable scenario-aware, adaptive network solutions.

Application of GNNs spans multiple wireless technologies and layers.

Abstract

The rapid advancement of communication technologies has driven the evolution of communication networks towards both high-dimensional resource utilization and multifunctional integration. This evolving complexity poses significant challenges in designing communication networks to satisfy the growing quality-of-service and time sensitivity of mobile applications in dynamic environments. Graph neural networks (GNNs) have emerged as fundamental deep learning (DL) models for complex communication networks. GNNs not only augment the extraction of features over network topologies but also enhance scalability and facilitate distributed computation. However, most existing GNNs follow a traditional passive learning framework, which may fail to meet the needs of increasingly diverse wireless systems. This survey proposes the employment of agentic artificial intelligence (AI) to organize and integrate GNNs, enabling scenario- and task-aware implementation towards edge general intelligence. To comprehend the full capability of GNNs, we holistically review recent applications of GNNs in wireless communications and networking. Specifically, we focus on the alignment between graph representations and network topologies, and between neural architectures and wireless tasks. We first provide an overview of GNNs based on prominent neural architectures, followed by the concept of agentic GNNs. Then, we summarize and compare GNN applications for conventional systems and emerging technologies, including physical, MAC, and network layer designs, integrated sensing and communication (ISAC), reconfigurable intelligent surface (RIS) and cell-free network architecture. We further propose a large language model (LLM) framework as an intelligent question-answering agent, leveraging this survey as a local knowledge base to enable GNN-related responses tailored to wireless communication research.